#instalación fiona
!pip install fiona

Collecting fiona
  Downloading fiona-1.10.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (56 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 56.6/56.6 kB 2.4 MB/s eta 0:00:00
Requirement already satisfied: attrs>=19.2.0 in /usr/local/lib/python3.10/dist-packages (from fiona) (24.2.0)
Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from fiona) (2024.8.30)
Requirement already satisfied: click~=8.0 in /usr/local/lib/python3.10/dist-packages (from fiona) (8.1.7)
Collecting click-plugins>=1.0 (from fiona)
  Downloading click_plugins-1.1.1-py2.py3-none-any.whl.metadata (6.4 kB)
Collecting cligj>=0.5 (from fiona)
  Downloading cligj-0.7.2-py3-none-any.whl.metadata (5.0 kB)
Downloading fiona-1.10.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.3 MB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 17.3/17.3 MB 76.9 MB/s eta 0:00:00
Downloading click_plugins-1.1.1-py2.py3-none-any.whl (7.5 kB)
Downloading cligj-0.7.2-py3-none-any.whl (7.1 kB)
Installing collected packages: cligj, click-plugins, fiona
Successfully installed click-plugins-1.1.1 cligj-0.7.2 fiona-1.10.1

# SE IMPORTAN LOS ARCHIVOS GPKG PASADOS DE JAPON (tarea 4)
import geopandas as gpd

Japon_Adm = gpd.read_file("https://github.com/brayan-lliuya-2024/TAREA_4-Grupo5/raw/refs/heads/main/maps/JaponAdm.gpkg")
japon_states = gpd.read_file("https://github.com/brayan-lliuya-2024/TAREA_4-Grupo5/raw/refs/heads/main/maps/JaponAdm.gpkg", layer="Estados")
japon_municipalities = gpd.read_file("https://github.com/brayan-lliuya-2024/TAREA_4-Grupo5/raw/refs/heads/main/maps/JaponAdm.gpkg", layer="Municipios")

/usr/local/lib/python3.10/dist-packages/pyogrio/geopandas.py:265: UserWarning: More than one layer found in 'JaponAdm.gpkg': 'Estados' (default), 'Municipios'. Specify layer parameter to avoid this warning.
  result = read_func(

# SE IMPORTA LA CAPA DE RIOS (tarea 4)
rivers = gpd.read_file("https://github.com/brayan-lliuya-2024/TAREA_4-Grupo5/raw/refs/heads/main/maps/MapasJapon.gpkg", layer="rivers_jp")

# SE IMPORTA LOS AEROPUERTOS DE JAPON
airports=gpd.read_file("https://github.com/brayan-lliuya-2024/TAREA_4-Grupo5/raw/refs/heads/main/maps/mapsjapan_ej2.gpkg",layer="airports")

# SE IMPORTA EL BORDE DEL PAÍS
border=gpd.read_file("https://github.com/brayan-lliuya-2024/TAREA_4-Grupo5/raw/refs/heads/main/maps/mapsjapan_ej3.gpkg")

# IMPORTANDO DATA DE PUERTOS A NIVEL MUNDIAL
import pandas as pd

portsFileLink="https://github.com/CienciaDeDatosEspacial/GeoDataFrame_Analytics/raw/main/data/UpdatedPub150.csv"
infoseaports=pd.read_csv(portsFileLink)

infoseaports.head(3)

#  SELECCIONAR SOLO COLUMNAS DE INTERÉS
infoseaports.rename(columns={'Main Port Name':'portName'},inplace=True)
infoseaports=infoseaports.loc[:,['portName', 'Country Code','Latitude', 'Longitude']]

infoseaports.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3739 entries, 0 to 3738
Data columns (total 4 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   portName      3739 non-null   object 
 1   Country Code  3739 non-null   object 
 2   Latitude      3739 non-null   float64
 3   Longitude     3739 non-null   float64
dtypes: float64(2), object(2)
memory usage: 117.0+ KB

# PROYECTANDO LA DATA
seaports=gpd.GeoDataFrame(data=infoseaports.copy(),
                           geometry=gpd.points_from_xy(infoseaports.Longitude,
                                                       infoseaports.Latitude),
                          crs=6684)

# separar solo para Japón
seaports_jap=seaports[seaports['Country Code']=='Japan'].copy()

# Reasignando índices
seaports_jap.reset_index(drop=True, inplace=True)

# reprojectando
seaports_jap_6684=seaports_jap.to_crs(6684)

seaports_jap_6684.plot(markersize=3)

<Axes: >

# Seleccionando solo aeropuestos grandes
largeA=airports[airports['kind']=='large_airport']
largeA.reset_index(drop=True, inplace=True)
largeAirports=largeA.to_crs(4326)

largeAirports.crs.axis_info

[Axis(name=Geodetic latitude, abbrev=Lat, direction=north, unit_auth_code=EPSG, unit_code=9122, unit_name=degree),
 Axis(name=Geodetic longitude, abbrev=Lon, direction=east, unit_auth_code=EPSG, unit_code=9122, unit_name=degree)]

# Ploteo
base=largeAirports.plot(color='red',marker="^")
seaports_jap_6684.plot(ax=base,alpha=0.5, markersize=3)

<Axes: >

seaports_jap_6684.head()

largeAirports.head()

# distancia entre 'Mombetsu Ko' y 'Narita International Airportl' en km
largeAirports.iloc[0].geometry.distance(seaports_jap_6684.iloc[0].geometry)/1000

1916.982917535566

# Distancias entre aeropuertos y puertos
seaports_jap_6684.geometry.apply\
(lambda g: largeAirports.geometry.distance(g)/1000)

# Asignar los nombres de aeropuertos y puertos como índices

seaports_jap_6684.set_index('portName').geometry.apply\
(lambda g: largeAirports.set_index('name').geometry.distance(g)/1000)

# Ordenar alfabeticamente filas y columnas
seaports_jap_6684.set_index('portName').geometry.apply\
(lambda g: largeAirports.set_index('name').geometry.distance(g)/1000).\
sort_index(axis=0).sort_index(axis=1)

distanceMatrixKM_sea_air= seaports_jap_6684.set_index('portName').geometry.apply\
                          (lambda g: largeAirports.set_index('name').geometry.distance(g)/1000).\
                          sort_index(axis=0).sort_index(axis=1)

# La distancia media desde un puerto marítimo hasta todos los grandes aeropuertos
distanceMatrixKM_sea_air.mean(axis=1).sort_values(ascending=True)

# distancias promedio, mínimas y máximas de los puertos hacia los aeropuertos
SomeStats=pd.DataFrame()
SomeStats['mean']=distanceMatrixKM_sea_air.mean(axis=1)
SomeStats['min']=distanceMatrixKM_sea_air.min(axis=1)
SomeStats['max']=distanceMatrixKM_sea_air.max(axis=1)

SomeStats.head(10)

# aeropuerto más alejado de cada puerto marítimo
distanceMatrixKM_sea_air.idxmax(axis=1)

# puerto marítimo más alejado de cada aeropuerto
distanceMatrixKM_sea_air.idxmax(axis=0)

# aeropuerto más cercano a cada puerto marítimo
distanceMatrixKM_sea_air.idxmin(axis=1)

# puerto marítimo más cercano a cada aeropuerto
distanceMatrixKM_sea_air.idxmin(axis=0)

rivers.head()

rivers

print(len(rivers['nam'].unique()))

382

#Se entiende a los datos que contienen "UNK" como desconocidos
rivers_with_unk = rivers[rivers.nam.str.contains('UNK')]
print(rivers_with_unk['nam'].unique())

['UNK' 'UNK\r\n' 'UNK\r\n\r\n' 'UNK    ISHIKARI GAWA TO TUNAGATTEIRUKAMO']

#Se filtrarán los ríos desconocidos ("UNK")
rivers = rivers[~rivers.nam.str.contains('UNK')]

#Se cambia "nam" a "name" para un mejor entendimiento
rivers = rivers.rename(columns={'nam': 'name'})

print(len(rivers['name'].unique()))

378

rivers.head()

rivers['name'].unique()

array(['ANBO G.', 'KIMOTSUKI G.', 'MANOSE G.', 'HIROTO G.', 'HISHIDA G.',
       'SHIN K.', 'SENDAI G.', 'OYODO G.', 'IWASE G.', 'HITOTSUSE G.',
       'OMARU G.', 'KUMA G.', 'MIMI G.', 'KAWABE G.', 'ISUZU G.',
       'MIDORI K.', 'KITA G.', 'SHIRA K.', 'KIKUCHI G.', 'BANJO G.',
       'ONO G.', 'SHIMANTO G.', 'MATSUDA G.', 'YABE G.', 'CHIKUGO G.',
       'OITA G.', 'KASE G.', 'KANOGAWA KO', 'HIJI G.', 'NIIDA G.',
       'MATSUURA G.', 'YAMAGUNI G.', 'YUSUHARA G.', 'YAKKAN G.',
       'HARAI K.', 'KOZA G.', 'NIYODO G.', 'NAHARI G.', 'ONGA G.',
       'KAIFU G.', 'AKI G.', 'HIKI G.', 'MANOBE G.', 'KUMANO G.',
       'YOSHINO G.', 'HYTkakunin', 'SHIGENOBU G.', 'NAKA G.',
       'NAKAYAMA G.', 'DOZUN G.', 'KAMO G.', 'TONDA G.', 'HOUOU KO',
       'IYA G.', 'KOTO G.', 'HIDAKA G.', 'KITAYAMA G.', 'ASA G.',
       'KATSUURA G.', 'YOSHIDA G.', 'FUSHINO G.', 'TOTSU K.', 'ARIDA G.',
       'SHIMATA G.', 'AKUI G.', 'ONO KO', 'SAITA G.', 'NISHIKI G.',
       'SABA G.', 'KINO G.', 'OZE G.', 'DOKI G.', 'ABU G.', 'KUROSE G.',
       'TAKATSU G.', 'AHIDA G.', 'NUTO G.', 'MIYA G.', 'ASHIDA G.',
       'YAMATO G.', 'HIKIMI G.', 'ODA G.', 'ASAHI G.', 'KUMODE G.',
       'IZUMO G.', 'KIKU G.', 'KUMOZUFURU K.', 'TENRYU G.', 'ENO K.',
       'OTA G.', 'NABARI G.', 'HAMANA KO', 'YODO G.', 'INA G.', 'OI G.',
       'TAKAHASHI G.', 'IGA G.', 'MISUMI G.', 'IBO G.', 'NARIWA G.',
       'SUFU G.', 'SAIJO G.', 'MUKO G.', 'CHIKUSA G.', 'KIZU G.',
       'UJI G.', 'HOZU G.', 'FUNAGIRA DAMUKO', 'SUZUKA G.', 'YAHAGI G.',
       'ABE K.', 'ICHI K.', 'KONO G.', 'KANNOSE G.', 'GONO G.', 'KETA G.',
       'KISO G.', 'KANDO G.', 'IBI G.', 'YUMESAKI G.', 'ETI G.',
       'NIKKO G.', 'ECHI G.', 'HII K.', 'SHONAI G.', 'OCHISE G.',
       'FUJI K.', 'KANSA G.', 'YASU G.', 'HINO G.', 'MIDORI KO',
       'MACHIYA G.', 'TOKI G.', 'SESSO KO', 'SHIZUMA G.', 'MARUYAMA G.',
       'IKAWA KO', 'YURA G.', 'SAKAWA G.', 'HAYA K.', 'ISUMI G.',
       'KOITO G.', 'TENJIN G.', 'INASHI G.', 'ADO G.', 'FUJI G.\r\n',
       'OBITSU G.', 'HAKUTA G.', 'SAGAMI G.', 'CYUKAI', 'SINJI KO',
       'YATA G.', 'MINAMI G.', 'TSURUMI G.', 'TAMA G.', 'YORO G.',
       'DASHI G.', 'FUEFUKI G.', 'SAKAI G.', 'KATSURA G.', 'KISHIDA G.',
       'NEO G.', 'MURAYAMA G.', 'KAMANASHI G.', 'ASA K.', 'EDO G.',
       'ARA K.', 'NAGARA G.', 'TAKENO G.', 'MASE G.', 'MASHITA G.',
       'TOUSENKYO KANAYAMA KO', 'INBA NUMA', 'OTAKI G.', 'TONE G.',
       'MIBU G.', 'MIURA TYOSUIIKE', 'FURUTONE G.', 'IRUMA G.',
       'KITA URA', 'KINU G,', 'KASUMI-GA URA', 'KOMA G.', 'MOTO ARA K.',
       'SUWA KO', 'ASUWA G.', 'CHIKUMA G.', 'NARAI G.', 'AZUSA KO',
       'KOKAI G.', 'OMOI G,', 'OMOI G.', 'SAKURA G.', 'KUZURYU G.',
       'AZUSA G.', 'WATARASE G.', 'TAISYO IKE', 'KABURA G.', 'KARASU G.',
       'KINU G.', 'TAKAHARA G.', 'HIMUMA', 'DAIYOJI G.', 'HINUMA G.',
       'SHIBAYAMA G.', 'TAKASE G.', 'KAKEHASHI G.', 'SAI G.', 'TEDORI G.',
       'KUJI G.', 'AGATSUMA G.', 'SHO K.', 'JINZU G.', 'JOZANJI G.',
       'SUSOBANA G.', 'JOUGANJI G.', 'KATASHINA G.', 'HIME K.', 'TA GAWA',
       'CHUZENJI KO', 'DAIYA G.', 'HOKI G.', 'KIYOTSU G.', 'SAME K.',
       'NAKATSU G.', 'SABI G.', 'NATSUI G.', 'SHINANO G.', 'SABAISHI G.',
       'HOKURA G.', 'ABUKUMA G.', 'TADAMI G.', 'KIDO G.', 'INAWASHIRO KO',
       'O KAWA', 'SHIBUMI G.', 'UKEDO G.', 'TOKONAMI G.',
       'NAKA NO KUCHI G.', 'AGANO G.', 'HIBARA KO', 'HAYADE G.',
       'MANO G.', 'HAYAMA KO', 'KAJI G.', 'TAINAI G.', 'SHIROSHI G.',
       'SHIROISHI G.', 'MOGAMI G.', 'MATSU K.', 'ABUKUBA G.', 'NATORI G.',
       'MIOMOTE G.', 'NANAKITA G.', 'HIROSE G.', 'SUKAWA G.',
       'KITAKAMI G.', 'EAI G.', 'OGUNI G.', 'HASAMA G.', 'AKA G.',
       'KESEN G.', 'MINASE G.', 'ISAWA G.', 'WAGA G.', 'KOYOSHI G.',
       'SARUKAISHI G.', 'OMONO G.', 'HEI G.', 'OMOTO G.', 'BABAME G.',
       'AKKA G.', 'ANI G.', 'YONESHIRO G.', 'UGE G.', 'MABECHI G.',
       'NIIBA G.', 'OIRASE G.', 'ASASEISHI G.', 'GONOHE G.', 'AISAKA G.',
       'ARA KAWA', 'SHITTSUNOHE G.', 'AKAISHI G.', 'NAKAMURA G.',
       'OGAWARA KO', 'IWAKI G.', 'TONABU G.', 'SHIRIUCHI G.', 'ASSABU G.',
       'HIDAKA HOROBETSU G.', 'YURAPPU G.', 'SHIZUNAI G.', 'MOTOURA K.',
       'TOYONI G.', 'SHIRIBESHI TOSHIBETSU G.', 'REKIFUNE G.',
       'APPETSU G.', 'OSARU G.', 'NIIKAPPU G.', 'SARU G.', 'MUKABIRA G.',
       'SHIRAOI G.', 'TOKACHI G.', 'SHIKOTSU KO', 'YUFUTSU G.', 'MU KAWA',
       'AZUMA G.', 'SATSUNAI G.', 'AZUMA G.\r\n\r\n', 'URAHORO G.',
       'ABIRA G.', 'SHIRIBETSU G.', 'CHITOSE G.', 'ONBETSU G.',
       'HORIKAPPU G.', 'SHIKARIBETSU G.', 'YUBARI G.', 'KUSHIRO G.',
       'TOYOHIRA G.', 'AKAN G.', 'OTOFUKE G.', 'EBETSU G.',
       'TOSHIBETSU G.', 'ISHIKARI G.', 'TOBETSU G.',
       'BARATO G.  ISHIKARI GAWA TO TUNAGATTEIRUKAMO', 'YOTCHI G.',
       'BIRIBETSU G.', 'SAHORO G.', 'CHARO G.', 'ASHORO G.',
       'BEKANBEUSHI G.', 'SHORO G.', 'SETSURI G.', 'FUREN G.', 'AKAN KO',
       'TOKOTAN G.', 'NISHIBETSU G.', 'ASHIBETSU G.', 'SHUNBETSU G.',
       'SORACHI G.', 'TOHORO G.', 'TOKORO G.', 'OKETO KO', 'SHIBETSU G.',
       'CHUBETSU G.', 'KUSSHARO KO', 'CHURUI G.', 'MUKA G.',
       'YANBETSU G.', 'SHARI G.', 'URYU G.', 'TOFUTSU KO', 'MOKOTO G.',
       'RUMAI G.', 'ABASHIRI  KO', 'URYUG.', 'YUBETSU G.',
       'OBIRASHIBE G.', 'SAROMABETSU G.', 'KOTANBETSU G.',
       'SHUMARINAI KO', 'NAYORO G.', 'HABORO G.', 'OKOPPE G.',
       'MOKOPPE G.', 'ENBETSU G.', 'TESHIO G.', 'TOKUSHIBETSU G.',
       'HOROBETSU G.', 'SAROBETSU G.', 'TONBETSU G.', 'RUBETSU G.',
       'KUCCHARO KO', 'SARUFUTSU G.', 'KOETOI G.', 'ONUMA'], dtype=object)

#Buscamos uno de ellos
rivers[rivers.name.str.contains('TOHORO')]

#Distancia de cada aeropuerto a TOHORO
rivers[rivers.name.str.contains('TOHORO')].iloc[0].geometry.distance(largeAirports.set_index('name').geometry)/1000

distanceMatrixKM_riv_air=rivers.set_index('name').geometry.apply\
(lambda g: largeAirports.set_index('name').geometry.distance(g)/1000).\
sort_index(axis=0).sort_index(axis=1)
distanceMatrixKM_riv_air

#Aquí, vemos una fila (río, nombre exacto), que indica la distancia a cada columna (gran aeropuerto)
distanceMatrixKM_riv_air.loc['TOHORO G.'].sort_values()

!pip install folium matplotlib mapclassify

from mapclassify import classify

Requirement already satisfied: folium in /usr/local/lib/python3.10/dist-packages (0.18.0)
Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (3.8.0)
Collecting mapclassify
  Downloading mapclassify-2.8.1-py3-none-any.whl.metadata (2.8 kB)
Requirement already satisfied: branca>=0.6.0 in /usr/local/lib/python3.10/dist-packages (from folium) (0.8.0)
Requirement already satisfied: jinja2>=2.9 in /usr/local/lib/python3.10/dist-packages (from folium) (3.1.4)
Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from folium) (1.26.4)
Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from folium) (2.32.3)
Requirement already satisfied: xyzservices in /usr/local/lib/python3.10/dist-packages (from folium) (2024.9.0)
Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.3.1)
Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (0.12.1)
Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (4.55.0)
Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.4.7)
Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (24.2)
Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (11.0.0)
Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (3.2.0)
Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (2.8.2)
Requirement already satisfied: networkx>=2.7 in /usr/local/lib/python3.10/dist-packages (from mapclassify) (3.4.2)
Requirement already satisfied: pandas!=1.5.0,>=1.4 in /usr/local/lib/python3.10/dist-packages (from mapclassify) (2.2.2)
Requirement already satisfied: scikit-learn>=1.0 in /usr/local/lib/python3.10/dist-packages (from mapclassify) (1.5.2)
Requirement already satisfied: scipy>=1.8 in /usr/local/lib/python3.10/dist-packages (from mapclassify) (1.13.1)
Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2>=2.9->folium) (3.0.2)
Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas!=1.5.0,>=1.4->mapclassify) (2024.2)
Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas!=1.5.0,>=1.4->mapclassify) (2024.2)
Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)
Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.0->mapclassify) (1.4.2)
Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.0->mapclassify) (3.5.0)
Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->folium) (3.4.0)
Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->folium) (3.10)
Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->folium) (2.2.3)
Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->folium) (2024.8.30)
Downloading mapclassify-2.8.1-py3-none-any.whl (59 kB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 59.1/59.1 kB 2.3 MB/s eta 0:00:00
Installing collected packages: mapclassify
Successfully installed mapclassify-2.8.1

base=largeAirports.explore(color='red',marker_kwds=dict(radius=10))
rivers[rivers.name.str.contains('TOHORO')].explore(m=base)

#Ahora, vamos a centrarnos en las varias polilíneas que se refieren a un mismo río.
rivers[~rivers.name.isna()]
#Esto sucede porque cada línea forma una parte de un mismo sistema de ríos

#Disolvamos los que pertenecen a un mismo sistema de ríos en una multilínea:
name=rivers.dissolve(by='name')
name

name.reset_index(drop=False,inplace=True)
name

#Dado que en conjunto representan un sistema de ríos, se cambiará el nombre
name = name.rename(columns={'name': 'system'})
name

#Otra matriz de distancias
distanceMatrixKM_sys_air=name.set_index('system').geometry.apply\
(lambda g: largeAirports.set_index('name').geometry.distance(g)/1000).\
sort_index(axis=0).sort_index(axis=1)

distanceMatrixKM_sys_air

#Se obtienen todas las distancias mínimas
mins=distanceMatrixKM_sys_air.idxmin(axis="columns") # same as axis=1
mins

#Se elige uno de ellos
mins.iloc[1]

'Yokota Air Base'

base=name.explore()
# the closest
largeAirports[largeAirports.name.isin(mins)].explore(m=base,color='red',marker_kwds=dict(radius=10))
# NOT the closest
largeAirports[~largeAirports.name.isin(mins)].explore(m=base,color='blue',marker_kwds=dict(radius=5))

name.convex_hull

name.convex_hull.plot()

<Axes: >

name_hulls=name.convex_hull.to_frame()
name_hulls['name']=name['system'].tolist()
name_hulls.rename(columns={0:'geometry'},inplace=True)
name_hulls=name_hulls.set_geometry('geometry')
name_hulls.crs="EPSG:6684"
name_hulls

#La matriz de distancias
distanceMatrixKM_sysHull_air=name_hulls.set_index('name').geometry.apply\
(lambda g: largeAirports.set_index('name').geometry.distance(g)/1000).\
sort_index(axis=0).sort_index(axis=1)

distanceMatrixKM_sysHull_air

mins=distanceMatrixKM_sysHull_air.idxmin(axis="columns")
mins

base=name_hulls.explore()
largeAirports[largeAirports.name.isin(mins)].explore(m=base,color='red',marker_kwds=dict(radius=10))
largeAirports[~largeAirports.name.isin(mins)].explore(m=base,color='blue',marker_kwds=dict(radius=5))

#Se usará el sistema disuelto
distanceMatrixKM_sys_air

# Escogiendo un rio , valor mínimo
distanceMatrixKM_sys_air.loc['ARA K.'].min()

11.965887088929707

minMts=distanceMatrixKM_sys_air.loc['ARA K.'].min()*50 # km

# Buffer del rio
rivers[rivers.name=='ARA K.'].buffer(distance = minMts)

# Visualización
bufferAroundARA=rivers[rivers.name=='ARA K.'].buffer(distance = minMts)
bufferAsBase=bufferAroundARA.explore(color='red')
rivers[rivers.name=='ARA K.'].explore(m=bufferAsBase,color='blue',style_kwds={'weight':1})

#  Importando los aeropuertos pequeños
small_airports=airports[airports['kind']=='small_airport']

# plotting
rivers[rivers.name=='ARA K.'].explore(m=bufferAsBase,color='blue',style_kwds={'weight':0.5})
small_airports.explore(m=bufferAsBase,color='black')

# Mantener los aeropuertos dentro del buffer
riversWithinBuffer=small_airports.clip(mask=bufferAroundARA)
riversWithinBuffer

# ploteo de aeropuertos dentro del buffer
bufferAsBase=bufferAroundARA.explore(color='red')
rivers[rivers.name=='ARA K.'].explore(m=bufferAsBase,color='blue',style_kwds={'weight':1})
riversWithinBuffer.explore(m=bufferAsBase,color='black')

# Mínimo de todos los mínimos por fila
distanceMatrixKM_sys_air.min(axis=1).min()

0.7952852405805073

# usando el valor anterior
minMinMts_5=5*distanceMatrixKM_sys_air.min(axis=1).min()*500

allMinBuffer=rivers.buffer(distance = minMinMts_5).explore(color='red')
rivers.explore(m=allMinBuffer,color='blue',style_kwds={'weight':0.75})

# Buffer de todos los ríos
riversAll_buf=rivers.buffer(distance = minMinMts_5)
riversAll_buf

# información
allRiversWithinBuffs=small_airports.clip(riversAll_buf)
allRiversWithinBuffs

# Ploteo
base=riversAll_buf.plot(color='yellow')
allRiversWithinBuffs.plot(ax=base, color='green', markersize=1)

<Axes: >

# Visualización
base=riversAll_buf.explore(color='yellow')
allRiversWithinBuffs.explore(m=base, color='green')

# Se carga el mapa de polígonos (la unidad administrativa más baja disponible)
units_admin = japon_states

# Se inspecciona las primeras filas
units_admin.head()

# Graficamos para verificar
units_admin.plot()

<Axes: >

units_admin.info

pandas.core.frame.DataFrame.infodef info(verbose: bool | None=None, buf: WriteBuffer[str] | None=None, max_cols: int | None=None, memory_usage: bool | str | None=None, show_counts: bool | None=None) -> None

/usr/local/lib/python3.10/dist-packages/pandas/core/frame.pyPrint a concise summary of a DataFrame.

This method prints information about a DataFrame including
the index dtype and columns, non-null values and memory usage.

Parameters
----------
verbose : bool, optional
    Whether to print the full summary. By default, the setting in
    ``pandas.options.display.max_info_columns`` is followed.
buf : writable buffer, defaults to sys.stdout
    Where to send the output. By default, the output is printed to
    sys.stdout. Pass a writable buffer if you need to further process
    the output.
max_cols : int, optional
    When to switch from the verbose to the truncated output. If the
    DataFrame has more than `max_cols` columns, the truncated output
    is used. By default, the setting in
    ``pandas.options.display.max_info_columns`` is used.
memory_usage : bool, str, optional
    Specifies whether total memory usage of the DataFrame
    elements (including the index) should be displayed. By default,
    this follows the ``pandas.options.display.memory_usage`` setting.

    True always show memory usage. False never shows memory usage.
    A value of 'deep' is equivalent to "True with deep introspection".
    Memory usage is shown in human-readable units (base-2
    representation). Without deep introspection a memory estimation is
    made based in column dtype and number of rows assuming values
    consume the same memory amount for corresponding dtypes. With deep
    memory introspection, a real memory usage calculation is performed
    at the cost of computational resources. See the
    :ref:`Frequently Asked Questions <df-memory-usage>` for more
    details.
show_counts : bool, optional
    Whether to show the non-null counts. By default, this is shown
    only if the DataFrame is smaller than
    ``pandas.options.display.max_info_rows`` and
    ``pandas.options.display.max_info_columns``. A value of True always
    shows the counts, and False never shows the counts.

Returns
-------
None
    This method prints a summary of a DataFrame and returns None.

See Also
--------
DataFrame.describe: Generate descriptive statistics of DataFrame
    columns.
DataFrame.memory_usage: Memory usage of DataFrame columns.

Examples
--------
>>> int_values = [1, 2, 3, 4, 5]
>>> text_values = ['alpha', 'beta', 'gamma', 'delta', 'epsilon']
>>> float_values = [0.0, 0.25, 0.5, 0.75, 1.0]
>>> df = pd.DataFrame({"int_col": int_values, "text_col": text_values,
...                   "float_col": float_values})
>>> df
    int_col text_col  float_col
0        1    alpha       0.00
1        2     beta       0.25
2        3    gamma       0.50
3        4    delta       0.75
4        5  epsilon       1.00

Prints information of all columns:

>>> df.info(verbose=True)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Data columns (total 3 columns):
 #   Column     Non-Null Count  Dtype
---  ------     --------------  -----
 0   int_col    5 non-null      int64
 1   text_col   5 non-null      object
 2   float_col  5 non-null      float64
dtypes: float64(1), int64(1), object(1)
memory usage: 248.0+ bytes

Prints a summary of columns count and its dtypes but not per column
information:

>>> df.info(verbose=False)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Columns: 3 entries, int_col to float_col
dtypes: float64(1), int64(1), object(1)
memory usage: 248.0+ bytes

Pipe output of DataFrame.info to buffer instead of sys.stdout, get
buffer content and writes to a text file:

>>> import io
>>> buffer = io.StringIO()
>>> df.info(buf=buffer)
>>> s = buffer.getvalue()
>>> with open("df_info.txt", "w",
...           encoding="utf-8") as f:  # doctest: +SKIP
...     f.write(s)
260

The `memory_usage` parameter allows deep introspection mode, specially
useful for big DataFrames and fine-tune memory optimization:

>>> random_strings_array = np.random.choice(['a', 'b', 'c'], 10 ** 6)
>>> df = pd.DataFrame({
...     'column_1': np.random.choice(['a', 'b', 'c'], 10 ** 6),
...     'column_2': np.random.choice(['a', 'b', 'c'], 10 ** 6),
...     'column_3': np.random.choice(['a', 'b', 'c'], 10 ** 6)
... })
>>> df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000000 entries, 0 to 999999
Data columns (total 3 columns):
 #   Column    Non-Null Count    Dtype
---  ------    --------------    -----
 0   column_1  1000000 non-null  object
 1   column_2  1000000 non-null  object
 2   column_3  1000000 non-null  object
dtypes: object(3)
memory usage: 22.9+ MB

>>> df.info(memory_usage='deep')
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000000 entries, 0 to 999999
Data columns (total 3 columns):
 #   Column    Non-Null Count    Dtype
---  ------    --------------    -----
 0   column_1  1000000 non-null  object
 1   column_2  1000000 non-null  object
 2   column_3  1000000 non-null  object
dtypes: object(3)
memory usage: 165.9 MB

import random
import pandas as pd
import geopandas as gpd

# Se crea una tabla de variables asociada a las unidades administrativas
# Las unidades administrativas tienen una columna "name" para poder asociar las variables.
variables_table = pd.DataFrame({
    "name": units_admin["ADM1_EN"],  # Tomar el nombre de la unidad administrativa
    "ADM1_JA": units_admin["ADM1_JA"], # Copiar la columna ADM1_JA de units_admin
    "population": [random.randint(1000, 5000) for _ in range(len(units_admin))],  # Ejemplo: población
    "area_km2": [random.uniform(10, 100) for _ in range(len(units_admin))],      # Ejemplo: área
    "density": [random.uniform(50, 300) for _ in range(len(units_admin))]        # Ejemplo: densidad
})

print(variables_table.head()) #Se observan las variables

      name ADM1_JA  population   area_km2     density
0    Aichi     愛知県        2407  75.351161  211.308909
1    Akita     秋田県        4310  64.664937  231.813474
2   Aomori     青森県        2091  52.093732   70.515923
3    Chiba     千葉県        3742  31.186209  136.697740
4    Ehime     愛媛県        1873  47.311352  101.132225

# Preprocesar las tablas
# Se asegura que ambas tablas tienen el mismo tipo en las columnas clave
units_admin["ADM1_JA"] = units_admin["ADM1_JA"].astype(str)
variables_table["ADM1_JA"] = variables_table["ADM1_JA"].astype(str)

units_admin.head()

# Asegurar que las columnas clave tienen el mismo tipo
units_admin["ADM1_JA"] = units_admin["ADM1_JA"].astype(str)
variables_table["ADM1_JA"] = variables_table["ADM1_JA"].astype(str)

# Se fucionan usando ADM1_JA
fusion_data = units_admin.merge(variables_table, on="ADM1_JA", how="left")

# Se verifica el resultado
print(fusion_data.head())

  ADM0_EN ADM0_JA ADM0_PCODE  ADM1_EN ADM1_JA ADM1_PCODE  \
0   Japan      日本         JP    Aichi     愛知県       JP23   
1   Japan      日本         JP    Akita     秋田県       JP05   
2   Japan      日本         JP   Aomori     青森県       JP02   
3   Japan      日本         JP    Chiba     千葉県       JP12   
4   Japan      日本         JP    Ehime     愛媛県       JP38   

                                            geometry name_x  name_y  \
0  MULTIPOLYGON (((1195512.712 1039956.796, 11953...    愛知県   aichi   
1  MULTIPOLYGON (((1376309.983 1576859.486, 13757...    秋田県   akita   
2  MULTIPOLYGON (((1488100.706 1763108.389, 14877...    青森県  aomori   
3  MULTIPOLYGON (((1466610.222 1182881.245, 14660...    千葉県   chiba   
4  MULTIPOLYGON (((801590.554 801225.044, 802058....    愛媛県   ehime   

   population   area_km2     density  
0        2407  75.351161  211.308909  
1        4310  64.664937  231.813474  
2        2091  52.093732   70.515923  
3        3742  31.186209  136.697740  
4        1873  47.311352  101.132225

# Finalmente, se guarda el resultado como un nuevo archivo GeoPackage
fusion_data.to_file("unidad_admin_fusionada.gpkg", driver="GPKG")

# Se visualiza el resultado
fusion_data.plot(column="area_km2", legend=True)

<Axes: >

# Instalar las librerías necesarias
!pip install libpysal esda splot

import geopandas as gpd
from libpysal.weights import KNN
from esda.moran import Moran
from splot.esda import moran_scatterplot
import matplotlib.pyplot as plt

Collecting libpysal
  Downloading libpysal-4.12.1-py3-none-any.whl.metadata (4.8 kB)
Collecting esda
  Downloading esda-2.6.0-py3-none-any.whl.metadata (2.0 kB)
Collecting splot
  Downloading splot-1.1.7-py3-none-any.whl.metadata (8.9 kB)
Requirement already satisfied: beautifulsoup4>=4.10 in /usr/local/lib/python3.10/dist-packages (from libpysal) (4.12.3)
Requirement already satisfied: geopandas>=0.10.0 in /usr/local/lib/python3.10/dist-packages (from libpysal) (1.0.1)
Requirement already satisfied: numpy>=1.22 in /usr/local/lib/python3.10/dist-packages (from libpysal) (1.26.4)
Requirement already satisfied: packaging>=22 in /usr/local/lib/python3.10/dist-packages (from libpysal) (24.2)
Requirement already satisfied: pandas>=1.4 in /usr/local/lib/python3.10/dist-packages (from libpysal) (2.2.2)
Requirement already satisfied: platformdirs>=2.0.2 in /usr/local/lib/python3.10/dist-packages (from libpysal) (4.3.6)
Requirement already satisfied: requests>=2.27 in /usr/local/lib/python3.10/dist-packages (from libpysal) (2.32.3)
Requirement already satisfied: scipy>=1.8 in /usr/local/lib/python3.10/dist-packages (from libpysal) (1.13.1)
Requirement already satisfied: shapely>=2.0.1 in /usr/local/lib/python3.10/dist-packages (from libpysal) (2.0.6)
Requirement already satisfied: scikit-learn>=1.1 in /usr/local/lib/python3.10/dist-packages (from libpysal) (1.5.2)
Collecting giddy (from splot)
  Downloading giddy-2.3.5-py3-none-any.whl.metadata (6.4 kB)
Requirement already satisfied: mapclassify in /usr/local/lib/python3.10/dist-packages (from splot) (2.8.1)
Requirement already satisfied: matplotlib>=3.3.3 in /usr/local/lib/python3.10/dist-packages (from splot) (3.8.0)
Requirement already satisfied: seaborn>=0.11.0 in /usr/local/lib/python3.10/dist-packages (from splot) (0.13.2)
Collecting spreg (from splot)
  Downloading spreg-1.8-py3-none-any.whl.metadata (1.7 kB)
Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4>=4.10->libpysal) (2.6)
Requirement already satisfied: pyogrio>=0.7.2 in /usr/local/lib/python3.10/dist-packages (from geopandas>=0.10.0->libpysal) (0.10.0)
Requirement already satisfied: pyproj>=3.3.0 in /usr/local/lib/python3.10/dist-packages (from geopandas>=0.10.0->libpysal) (3.7.0)
Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.3->splot) (1.3.1)
Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.3->splot) (0.12.1)
Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.3->splot) (4.55.0)
Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.3->splot) (1.4.7)
Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.3->splot) (11.0.0)
Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.3->splot) (3.2.0)
Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.3->splot) (2.8.2)
Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.4->libpysal) (2024.2)
Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.4->libpysal) (2024.2)
Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.27->libpysal) (3.4.0)
Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.27->libpysal) (3.10)
Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.27->libpysal) (2.2.3)
Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.27->libpysal) (2024.8.30)
Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.1->libpysal) (1.4.2)
Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.1->libpysal) (3.5.0)
Collecting quantecon>=0.4.7 (from giddy->splot)
  Downloading quantecon-0.7.2-py3-none-any.whl.metadata (4.9 kB)
Requirement already satisfied: networkx>=2.7 in /usr/local/lib/python3.10/dist-packages (from mapclassify->splot) (3.4.2)
Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=3.3.3->splot) (1.16.0)
Requirement already satisfied: numba>=0.49.0 in /usr/local/lib/python3.10/dist-packages (from quantecon>=0.4.7->giddy->splot) (0.60.0)
Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from quantecon>=0.4.7->giddy->splot) (1.13.1)
Requirement already satisfied: llvmlite<0.44,>=0.43.0dev0 in /usr/local/lib/python3.10/dist-packages (from numba>=0.49.0->quantecon>=0.4.7->giddy->splot) (0.43.0)
Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->quantecon>=0.4.7->giddy->splot) (1.3.0)
Downloading libpysal-4.12.1-py3-none-any.whl (2.8 MB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.8/2.8 MB 25.7 MB/s eta 0:00:00
Downloading esda-2.6.0-py3-none-any.whl (135 kB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 135.4/135.4 kB 10.0 MB/s eta 0:00:00
Downloading splot-1.1.7-py3-none-any.whl (39 kB)
Downloading giddy-2.3.5-py3-none-any.whl (61 kB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 61.1/61.1 kB 4.5 MB/s eta 0:00:00
Downloading spreg-1.8-py3-none-any.whl (379 kB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 379.8/379.8 kB 23.8 MB/s eta 0:00:00
Downloading quantecon-0.7.2-py3-none-any.whl (215 kB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 215.4/215.4 kB 15.7 MB/s eta 0:00:00
Installing collected packages: quantecon, libpysal, spreg, esda, giddy, splot
Successfully installed esda-2.6.0 giddy-2.3.5 libpysal-4.12.1 quantecon-0.7.2 splot-1.1.7 spreg-1.8

Variables numéricas en el dataset: ['latitude_deg', 'longitude_deg', 'elevation_ft']

   latitude_deg  longitude_deg  elevation_ft
0     24.396389     124.245000         102.0
1     27.090231     142.192032           NaN
2     35.623658     139.894506          16.0
3     44.439286     141.400487           NaN
4     35.342222     139.656389           4.0

/usr/local/lib/python3.10/dist-packages/libpysal/weights/distance.py:153: UserWarning: The weights matrix is not fully connected: 
 There are 2 disconnected components.
  W.__init__(self, neighbors, id_order=ids, **kwargs)

Coeficiente de Moran: 0.9916672561677757
Valor p-simulado: 0.001

# Instalar las librerías necesarias
!pip install libpysal esda splot

import geopandas as gpd
from libpysal.weights import KNN
from esda.moran import Moran
from splot.esda import moran_scatterplot
import matplotlib.pyplot as plt

Collecting libpysal
  Downloading libpysal-4.12.1-py3-none-any.whl.metadata (4.8 kB)
Collecting esda
  Downloading esda-2.6.0-py3-none-any.whl.metadata (2.0 kB)
Collecting splot
  Downloading splot-1.1.7-py3-none-any.whl.metadata (8.9 kB)
Requirement already satisfied: beautifulsoup4>=4.10 in /usr/local/lib/python3.10/dist-packages (from libpysal) (4.12.3)
Requirement already satisfied: geopandas>=0.10.0 in /usr/local/lib/python3.10/dist-packages (from libpysal) (1.0.1)
Requirement already satisfied: numpy>=1.22 in /usr/local/lib/python3.10/dist-packages (from libpysal) (1.26.4)
Requirement already satisfied: packaging>=22 in /usr/local/lib/python3.10/dist-packages (from libpysal) (24.2)
Requirement already satisfied: pandas>=1.4 in /usr/local/lib/python3.10/dist-packages (from libpysal) (2.2.2)
Requirement already satisfied: platformdirs>=2.0.2 in /usr/local/lib/python3.10/dist-packages (from libpysal) (4.3.6)
Requirement already satisfied: requests>=2.27 in /usr/local/lib/python3.10/dist-packages (from libpysal) (2.32.3)
Requirement already satisfied: scipy>=1.8 in /usr/local/lib/python3.10/dist-packages (from libpysal) (1.13.1)
Requirement already satisfied: shapely>=2.0.1 in /usr/local/lib/python3.10/dist-packages (from libpysal) (2.0.6)
Requirement already satisfied: scikit-learn>=1.1 in /usr/local/lib/python3.10/dist-packages (from libpysal) (1.5.2)
Collecting giddy (from splot)
  Downloading giddy-2.3.5-py3-none-any.whl.metadata (6.4 kB)
Requirement already satisfied: mapclassify in /usr/local/lib/python3.10/dist-packages (from splot) (2.8.1)
Requirement already satisfied: matplotlib>=3.3.3 in /usr/local/lib/python3.10/dist-packages (from splot) (3.8.0)
Requirement already satisfied: seaborn>=0.11.0 in /usr/local/lib/python3.10/dist-packages (from splot) (0.13.2)
Collecting spreg (from splot)
  Downloading spreg-1.8-py3-none-any.whl.metadata (1.7 kB)
Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4>=4.10->libpysal) (2.6)
Requirement already satisfied: pyogrio>=0.7.2 in /usr/local/lib/python3.10/dist-packages (from geopandas>=0.10.0->libpysal) (0.10.0)
Requirement already satisfied: pyproj>=3.3.0 in /usr/local/lib/python3.10/dist-packages (from geopandas>=0.10.0->libpysal) (3.7.0)
Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.3->splot) (1.3.1)
Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.3->splot) (0.12.1)
Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.3->splot) (4.55.0)
Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.3->splot) (1.4.7)
Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.3->splot) (11.0.0)
Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.3->splot) (3.2.0)
Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.3->splot) (2.8.2)
Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.4->libpysal) (2024.2)
Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.4->libpysal) (2024.2)
Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.27->libpysal) (3.4.0)
Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.27->libpysal) (3.10)
Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.27->libpysal) (2.2.3)
Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.27->libpysal) (2024.8.30)
Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.1->libpysal) (1.4.2)
Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.1->libpysal) (3.5.0)
Collecting quantecon>=0.4.7 (from giddy->splot)
  Downloading quantecon-0.7.2-py3-none-any.whl.metadata (4.9 kB)
Requirement already satisfied: networkx>=2.7 in /usr/local/lib/python3.10/dist-packages (from mapclassify->splot) (3.4.2)
Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=3.3.3->splot) (1.16.0)
Requirement already satisfied: numba>=0.49.0 in /usr/local/lib/python3.10/dist-packages (from quantecon>=0.4.7->giddy->splot) (0.60.0)
Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from quantecon>=0.4.7->giddy->splot) (1.13.1)
Requirement already satisfied: llvmlite<0.44,>=0.43.0dev0 in /usr/local/lib/python3.10/dist-packages (from numba>=0.49.0->quantecon>=0.4.7->giddy->splot) (0.43.0)
Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->quantecon>=0.4.7->giddy->splot) (1.3.0)
Downloading libpysal-4.12.1-py3-none-any.whl (2.8 MB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.8/2.8 MB 25.7 MB/s eta 0:00:00
Downloading esda-2.6.0-py3-none-any.whl (135 kB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 135.4/135.4 kB 10.0 MB/s eta 0:00:00
Downloading splot-1.1.7-py3-none-any.whl (39 kB)
Downloading giddy-2.3.5-py3-none-any.whl (61 kB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 61.1/61.1 kB 4.5 MB/s eta 0:00:00
Downloading spreg-1.8-py3-none-any.whl (379 kB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 379.8/379.8 kB 23.8 MB/s eta 0:00:00
Downloading quantecon-0.7.2-py3-none-any.whl (215 kB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 215.4/215.4 kB 15.7 MB/s eta 0:00:00
Installing collected packages: quantecon, libpysal, spreg, esda, giddy, splot
Successfully installed esda-2.6.0 giddy-2.3.5 libpysal-4.12.1 quantecon-0.7.2 splot-1.1.7 spreg-1.8

# Cargar el shapefile o geopackage con los datos espaciales
datadisMap = gpd.read_file(
    "https://github.com/brayan-lliuya-2024/TAREA_4-Grupo5/raw/refs/heads/main/maps/mapsjapan_ej2.gpkg"
)

datadisMap.head()

datadisMap.dtypes

# Para observar la cantidad de variables numericas
numeric_columns = datadisMap.select_dtypes(include=['float64', 'int64']).columns
print(f"Variables numéricas en el dataset: {list(numeric_columns)}")

Variables numéricas en el dataset: ['latitude_deg', 'longitude_deg', 'elevation_ft']

# Verificar las nuevas variables
print(datadisMap[['latitude_deg', 'longitude_deg', 'elevation_ft']].head())

   latitude_deg  longitude_deg  elevation_ft
0     24.396389     124.245000         102.0
1     27.090231     142.192032           NaN
2     35.623658     139.894506          16.0
3     44.439286     141.400487           NaN
4     35.342222     139.656389           4.0

# Seleccionar una de las variables numéricas
variable = "latitude_deg"

# Crear matriz de vecinos usando KNN (K=8)
k = 8
w_knn8 = KNN.from_dataframe(datadisMap, k=k)

/usr/local/lib/python3.10/dist-packages/libpysal/weights/distance.py:153: UserWarning: The weights matrix is not fully connected: 
 There are 2 disconnected components.
  W.__init__(self, neighbors, id_order=ids, **kwargs)

# Transformar los pesos para que sumen 1
w_knn8.transform = "R"

from libpysal.weights.distance import KNN

# Calcular el coeficiente de Moran para la variable seleccionada
moran = Moran(datadisMap[variable], w_knn8)
print(f"Coeficiente de Moran: {moran.I}")
print(f"Valor p-simulado: {moran.p_sim}")

Coeficiente de Moran: 0.9916672561677757
Valor p-simulado: 0.001

# Crear el diagrama de dispersión de Moran
fig, ax = moran_scatterplot(moran, aspect_equal=True)
ax.set_xlabel(f"{variable}_std")
ax.set_ylabel(f"SpatialLag_{variable}_std")
ax.set_title(f"Diagrama de Dispersión de Moran - {variable}")
plt.show()

	World Port Index Number	Region Name	Main Port Name	Country Code	World Water Body	Sailing Direction or Publication	Publication Link	...	Supplies - Fuel Oil	Supplies - Diesel Oil	Supplies - Aviation Fuel	Supplies - Deck	Supplies - Engine	Repairs	Dry Dock	Railway	Latitude	Longitude
0	7950.0	United States E Coast -- 6585	Maurer	United States	North Atlantic Ocean	U.S. Coast Pilot 2 - Atlantic Coast: Cape Cod ...	https://nauticalcharts.noaa.gov/publications/c...	...	Yes	Yes	Unknown	Yes	Yes	Moderate	Unknown	Unknown	40.533333	-74.250000
1	52235.0	Sulawesi -- 51970	Mangkasa Oil Terminal	Indonesia	Teluk Bone; Banda Sea; South Pacific Ocean	Sailing Directions Pub. 163 (Enroute) - Borneo...	https://msi.geo.nga.mil/api/publications/downl...	...	No	No	Unknown	No	No	NaN	Unknown	Unknown	-2.733333	121.066667
2	47620.0	Madagascar -- 47350	Iharana	Madagascar	Indian Ocean	Sailing Directions Pub. 171 (Enroute) - East A...	https://msi.geo.nga.mil/api/publications/downl...	...	No	No	Unknown	No	No	Emergency Only	Unknown	Unknown	-13.350000	50.000000

	portName	Country Code	Latitude	Longitude	geometry
0	Mombetsu Ko	Japan	44.350000	143.350000	POINT (143.35 44.35)
1	Iwakuni Ko	Japan	34.183333	132.250000	POINT (132.25 34.183)
2	Hitachi	Japan	36.500000	140.633333	POINT (140.633 36.5)
3	Kushiro Ko	Japan	42.983333	144.366667	POINT (144.367 42.983)
4	Hanasaki Ko	Japan	43.283333	145.583333	POINT (145.583 43.283)

	name	kind	latitude_deg	longitude_deg	elevation_ft	iso_region	municipality	geometry
0	Narita International Airport	large_airport	35.764702	140.386002	141.0	JP-12	Narita	POINT (1487953.425 1208863.893)
1	Kansai International Airport	large_airport	34.427299	135.244003	26.0	JP-27	Osaka	POINT (1035834.881 992037.816)
2	New Chitose Airport	large_airport	42.775200	141.692001	82.0	JP-01	Sapporo	POINT (1449235.049 2015248.441)
3	Fukuoka Airport	large_airport	33.585899	130.451004	32.0	JP-40	Fukuoka	POINT (599284.927 859521.885)
4	Kagoshima Airport	large_airport	31.803400	130.718994	906.0	JP-46	Kagoshima	POINT (636838.567 662888.116)

	0	1	2	3	4	5	6	7	8	9	10	11
0	1916.982918	1434.122723	2482.118909	1047.698556	919.098829	1580.202859	1473.005814	2101.240880	1856.072084	1832.986905	365.375780	378.854480
1	1916.997943	1434.137771	2482.133644	1047.713243	919.113851	1580.217911	1473.020856	2101.255919	1856.087113	1833.001949	365.387595	378.866695
2	1916.989976	1434.130115	2482.126869	1047.706549	919.106372	1580.210132	1473.013259	2101.248347	1856.079158	1832.994072	365.379065	378.858098
3	1916.982990	1434.122934	2482.119425	1047.699095	919.099111	1580.203018	1473.006049	2101.241126	1856.072163	1832.987021	365.374866	378.853630
4	1916.981857	1434.121848	2482.118472	1047.698153	919.098051	1580.201914	1473.004972	2101.240052	1856.071031	1832.985901	365.373631	378.852387
...	...	...	...	...	...	...	...	...	...	...	...	...
158	1916.992567	1434.132681	2482.129318	1047.708986	919.108920	1580.212709	1473.015820	2101.250905	1856.081748	1832.996657	365.381301	378.860391
159	1916.999123	1434.138895	2482.134616	1047.714202	919.114943	1580.219056	1473.021969	2101.257028	1856.088290	1833.003112	365.388927	378.868032
160	1916.994056	1434.134000	2482.130225	1047.709858	919.110149	1580.214094	1473.017108	2101.252181	1856.083230	1832.998095	365.383681	378.862733
161	1916.991494	1434.131654	2482.128421	1047.708100	919.107920	1580.211664	1473.014802	2101.249891	1856.080677	1832.995597	365.380118	378.859201
162	1916.984800	1434.124572	2482.120640	1047.700274	919.100657	1580.204722	1473.007656	2101.242719	1856.073965	1832.988779	365.377511	378.856244

name	Narita International Airport	Kansai International Airport	New Chitose Airport	Fukuoka Airport	Kagoshima Airport	Chubu Centrair International Airport	Osaka International Airport	Sendai Airport	Tokyo Haneda International Airport	Yokota Air Base	Naha Airport / JASDF Naha Air Base	Kadena Air Base
portName
Mombetsu Ko	1916.982918	1434.122723	2482.118909	1047.698556	919.098829	1580.202859	1473.005814	2101.240880	1856.072084	1832.986905	365.375780	378.854480
Iwakuni Ko	1916.997943	1434.137771	2482.133644	1047.713243	919.113851	1580.217911	1473.020856	2101.255919	1856.087113	1833.001949	365.387595	378.866695
Hitachi	1916.989976	1434.130115	2482.126869	1047.706549	919.106372	1580.210132	1473.013259	2101.248347	1856.079158	1832.994072	365.379065	378.858098
Kushiro Ko	1916.982990	1434.122934	2482.119425	1047.699095	919.099111	1580.203018	1473.006049	2101.241126	1856.072163	1832.987021	365.374866	378.853630
Hanasaki Ko	1916.981857	1434.121848	2482.118472	1047.698153	919.098051	1580.201914	1473.004972	2101.240052	1856.071031	1832.985901	365.373631	378.852387
...	...	...	...	...	...	...	...	...	...	...	...	...
Shimizu Ko	1916.992567	1434.132681	2482.129318	1047.708986	919.108920	1580.212709	1473.015820	2101.250905	1856.081748	1832.996657	365.381301	378.860391
Shimonoseki	1916.999123	1434.138895	2482.134616	1047.714202	919.114943	1580.219056	1473.021969	2101.257028	1856.088290	1833.003112	365.388927	378.868032
Tsuruga Ko	1916.994056	1434.134000	2482.130225	1047.709858	919.110149	1580.214094	1473.017108	2101.252181	1856.083230	1832.998095	365.383681	378.862733
Yokosuka Ko	1916.991494	1434.131654	2482.128421	1047.708100	919.107920	1580.211664	1473.014802	2101.249891	1856.080677	1832.995597	365.380118	378.859201
Wakkanai	1916.984800	1434.124572	2482.120640	1047.700274	919.100657	1580.204722	1473.007656	2101.242719	1856.073965	1832.988779	365.377511	378.856244

TAREA 6 - Grupo 5 (Japan)¶

Extracción de Ubicaciones Espaciales¶

Distancia entre puntos¶

Ejercicio 2¶

Ejercicio 3¶

Distancia usando buffers¶

Ejercicio 4¶

Distancia usando buffers¶

Ejercicio 5¶

Ejercicio 7¶

name	Chubu Centrair International Airport	Fukuoka Airport	Kadena Air Base	Kagoshima Airport	Kansai International Airport	Naha Airport / JASDF Naha Air Base	Narita International Airport	New Chitose Airport	Osaka International Airport	Sendai Airport	Tokyo Haneda International Airport	Yokota Air Base
portName
Abashiri Ko	1580.202389	1047.698295	378.853592	919.098423	1434.122279	365.374874	1916.982403	2482.118635	1473.005383	2101.240455	1856.071573	1832.986413
Aioi	1580.215862	1047.711484	378.864424	919.111882	1434.135755	365.385341	1916.995845	2482.131863	1473.018856	2101.253926	1856.085017	1832.999874
Akita-Funakawa Ko	1580.208395	1047.704211	378.858327	919.104434	1434.128291	365.379441	1916.988387	2482.124566	1473.011396	2101.246468	1856.077558	1832.992409
Amagasaki	1580.215237	1047.711028	378.863523	919.111307	1434.135151	365.384433	1916.995186	2482.131395	1473.018262	2101.253336	1856.084361	1832.999232
Aokata	1580.221079	1047.716048	378.869997	919.116921	1434.140900	365.390858	1917.001171	2482.136477	1473.023966	2101.259020	1856.090336	1833.005148
...	...	...	...	...	...	...	...	...	...	...	...	...
Yawatahama	1580.218278	1047.713750	378.866616	919.114264	1434.138158	365.387483	1916.998276	2482.134142	1473.021252	2101.256319	1856.087448	1833.002299
Yokkaichi	1580.214130	1047.710094	378.862250	919.110249	1434.134064	365.383166	1916.994047	2482.130449	1473.017184	2101.252262	1856.083225	1832.998108
Yokohama Ko	1580.211615	1047.708011	378.859264	919.107857	1434.131599	365.380189	1916.991453	2482.128334	1473.014744	2101.249832	1856.080636	1832.995552
Yokosuka Ko	1580.211664	1047.708100	378.859201	919.107920	1434.131654	365.380118	1916.991494	2482.128421	1473.014802	2101.249891	1856.080677	1832.995597
Yura	1580.215950	1047.711805	378.863892	919.112045	1434.135874	365.384770	1916.995879	2482.132169	1473.018990	2101.254066	1856.085055	1832.999935

	0
portName
Nemuro Ko	1448.978992
Hanasaki Ko	1448.979022
Abashiri Ko	1448.979559
Mombetsu Ko	1448.980061
Kushiro Ko	1448.980119
...	...
Nakagusuku	1449.002571
Nishihara	1449.002689
Naha Ko	1449.002777
Hirara Ko	1449.005437
Ishigaki	1449.006559

	f_code	hyc	lit	nam	soc	geometry
0	BH140	8	1	UNK	JPN	LINESTRING (373410.491 44638.656, 373749.791 4...
1	BH140	8	1	UNK	JPN	LINESTRING (373749.791 44838.884, 373376.037 4...
2	BH140	8	1	UNK	JPN	LINESTRING (379093.829 45402.433, 379850.108 4...
3	BH140	8	1	UNK	JPN	LINESTRING (373749.791 44838.884, 375023.404 4...
4	BH140	8	1	UNK	JPN	LINESTRING (375437.64 45633.307, 375217.713 45...

	f_code	hyc	lit	name	soc	geometry
18	BH140	8	1	ANBO G.	JPN	LINESTRING (640341.959 494783.487, 640533.83 4...
23	BH140	8	1	KIMOTSUKI G.	JPN	LINESTRING (668114.864 615221.236, 666979.228 ...
25	BH140	8	1	KIMOTSUKI G.	JPN	LINESTRING (663537.594 615098.715, 661949.242 ...
26	BH140	8	1	MANOSE G.	JPN	LINESTRING (608555.528 613449.112, 608760.762 ...
27	BH140	8	1	MANOSE G.	JPN	LINESTRING (599901.222 620731.187, 600053.928 ...

	geometry
name
Narita International Airport	964.139995
Kansai International Airport	1328.822323
New Chitose Airport	270.397139
Fukuoka Airport	1686.891758
Kagoshima Airport	1820.218243
Chubu Centrair International Airport	1207.474836
Osaka International Airport	1285.612683
Sendai Airport	696.895538
Tokyo Haneda International Airport	1007.961540
Yokota Air Base	1004.373970
Naha Airport / JASDF Naha Air Base	2501.407196
Kadena Air Base	2480.169216

name	Chubu Centrair International Airport	Fukuoka Airport	Kadena Air Base	Kagoshima Airport	Kansai International Airport	Naha Airport / JASDF Naha Air Base	Narita International Airport	New Chitose Airport	Osaka International Airport	Sendai Airport	Tokyo Haneda International Airport	Yokota Air Base
name
ABASHIRI KO	1217.283686	1677.182631	2480.229305	1816.532215	1333.154795	2501.435605	990.433074	244.551514	1289.520308	718.752026	1031.244435	1024.571465
ABASHIRI KO	1221.450033	1681.065622	2484.413919	1820.660498	1337.342462	2505.620110	994.375482	248.060198	1293.708457	722.761821	1035.246731	1028.624369
ABASHIRI KO	1221.883647	1681.065622	2484.483807	1820.660498	1337.587105	2505.688622	995.366575	248.060198	1293.941167	723.638463	1036.136728	1029.413953
ABE K.	145.207525	752.649206	1404.981922	802.095667	296.588420	1426.017681	208.081027	934.788240	272.380742	429.855373	146.885518	127.463080
ABE K.	147.570407	755.686219	1416.144168	808.983507	300.281871	1437.214887	197.428037	898.156506	273.393886	398.721079	137.065171	106.327763
...	...	...	...	...	...	...	...	...	...	...	...	...
YURA G.	136.271003	489.826883	1232.644105	580.834985	93.659352	1253.887470	463.826545	1014.210373	52.156038	598.567185	405.740026	367.973183
YURA G.	146.116346	474.084438	1223.352566	568.810180	96.626687	1244.586170	471.604453	1015.365659	57.933202	602.928759	413.801842	375.619762
YURA G.	132.514027	498.133515	1237.317549	586.957292	95.367980	1258.564476	459.421105	1012.425724	53.114370	594.698752	401.353183	363.562105
YURAPPU G.	891.326203	1302.876721	2119.461831	1450.901618	988.902091	2140.558714	738.826818	131.905972	944.783387	472.158064	763.389468	743.754020
YUSUHARA G.	393.011867	232.048127	913.742596	263.398089	240.533547	934.955208	740.857837	1306.427070	275.752309	902.421690	679.818443	649.522678

	geometry	f_code	hyc	lit	soc
name
ABASHIRI KO	MULTILINESTRING ((1619681.843 2195475.388, 161...	BH140	8	2	JPN
ABE K.	MULTILINESTRING ((1319703.289 1085929.375, 131...	BH140	8	1	JPN
ABIRA G.	LINESTRING (1456120.306 2002642.709, 1456709.7...	BH140	8	1	JPN
ABU G.	MULTILINESTRING ((696983.361 949047.64, 696948...	BH140	8	1	JPN
ABUKUBA G.	LINESTRING (1480155.147 1474218.862, 1480677.6...	BH140	8	2	JPN
...	...	...	...	...	...
YUFUTSU G.	LINESTRING (1428896.05 2007936.152, 1429050.3 ...	BH140	8	1	JPN
YUMESAKI G.	LINESTRING (976663.994 1025342.223, 975670.044...	BH140	8	1	JPN
YURA G.	MULTILINESTRING ((1040815.46 1085564.646, 1041...	BH140	8	1	JPN
YURAPPU G.	LINESTRING (1333769.036 1936014.009, 1333840.8...	BH140	8	1	JPN
YUSUHARA G.	LINESTRING (837832.473 833123.256, 838060.352 ...	BH140	8	1	JPN

name	Chubu Centrair International Airport	Fukuoka Airport	Kadena Air Base	Kagoshima Airport	Kansai International Airport	Naha Airport / JASDF Naha Air Base	Narita International Airport	New Chitose Airport	Osaka International Airport	Sendai Airport	Tokyo Haneda International Airport	Yokota Air Base
system
ABASHIRI KO	1217.283686	1677.182631	2480.229305	1816.532215	1333.154795	2501.435605	990.433074	244.551514	1289.520308	718.752026	1031.244435	1024.571465
ABE K.	144.983225	752.549728	1404.981922	802.095667	296.530054	1426.017681	197.428037	898.156506	272.126096	398.721079	137.065171	106.327763
ABIRA G.	986.107165	1428.597944	2236.137954	1570.402794	1094.514545	2257.309983	794.416866	9.515936	1050.567774	519.663076	827.203575	813.805987
ABU G.	471.180435	126.047354	958.063141	290.426400	324.278622	978.679111	813.569313	1285.798056	343.992731	934.729471	754.546203	718.136116
ABUKUBA G.	516.816790	1074.143714	1806.245385	1170.230932	655.682134	1827.452077	265.057045	538.129229	615.991719	5.783667	301.731212	295.988611
...	...	...	...	...	...	...	...	...	...	...	...	...
YUFUTSU G.	983.783901	1416.725077	2227.784381	1560.932181	1089.287158	2248.933151	791.841251	9.936390	1045.255579	517.076335	824.669051	811.313853
YUMESAKI G.	196.752862	412.020326	1149.985786	496.844342	67.899759	1171.219711	533.547005	1066.577779	71.687692	665.207353	475.023172	437.853553
YURA G.	132.514027	473.345685	1223.352566	568.673177	93.659352	1244.586170	459.421105	995.587692	52.156038	591.629144	401.353183	363.562105
YURAPPU G.	891.326203	1302.876721	2119.461831	1450.901618	988.902091	2140.558714	738.826818	131.905972	944.783387	472.158064	763.389468	743.754020
YUSUHARA G.	393.011867	232.048127	913.742596	263.398089	240.533547	934.955208	740.857837	1306.427070	275.752309	902.421690	679.818443	649.522678

	0
0	POLYGON ((1619821.192 2190479.177, 1619655.376...
1	POLYGON ((1319703.289 1085929.375, 1319291.634...
2	POLYGON ((1456120.306 2002642.709, 1459254.619...
3	POLYGON ((699425.217 946543.319, 698896.171 94...
4	POLYGON ((1489593.48 1473915.864, 1480155.147 ...
...	...
373	POLYGON ((1456782.408 2000091.379, 1456330.922...
374	POLYGON ((976663.994 1025342.223, 975670.044 1...
375	POLYGON ((1040815.46 1085564.646, 1014604.814 ...
376	POLYGON ((1342483.195 1933283.827, 1339369.542...
377	POLYGON ((837832.473 833123.256, 831300.539 85...

	0
1864	POLYGON ((1439908.441 1195134.728, 1439919.852...
1869	POLYGON ((1439498.477 1195512.157, 1439539.668...
1882	POLYGON ((1436961.577 1197909.268, 1438127.45 ...
1918	POLYGON ((1429488.988 1200446.419, 1429819.741...
2036	POLYGON ((1410331.337 1211455.729, 1410387.052...
2118	POLYGON ((1402890.236 1221662.765, 1403827.204...
2159	POLYGON ((1401719.121 1222743.958, 1401690.837...
2235	POLYGON ((1386093.467 1235060.938, 1386509.885...
2236	POLYGON ((1363369.923 1224412.622, 1363390.125...
2237	POLYGON ((1383360.896 1234649.125, 1383404.249...
3350	POLYGON ((1363213.181 1457258.844, 1363146.259...

	mean	min	max
portName
Abashiri Ko	1448.979559	365.374874	2482.118635
Aioi	1448.992511	365.385341	2482.131863
Akita-Funakawa Ko	1448.985324	365.379441	2482.124566
Amagasaki	1448.991871	365.384433	2482.131395
Aokata	1448.997660	365.390858	2482.136477
Aomori Ko	1448.984137	365.378631	2482.123282
Atsumi	1448.990624	365.382756	2482.130439
Beppu	1448.995615	365.388390	2482.134776
Chiba Ko	1448.987813	365.379697	2482.127944
Choshi	1448.987133	365.378905	2482.127365

	name	kind	latitude_deg	longitude_deg	elevation_ft	iso_region	municipality	geometry
403	Kumagaya Flying Club Osato Airfield	small_airport	36.112460	139.413100	NaN	JP-11	Kumagaya	POINT (1392627.42 1233045.482)
3029	Kumagaya Trike Club Airfield	small_airport	36.121580	139.400290	86.0	JP-11	Kumagaya	POINT (1391295.745 1233878.823)
320	Honda Airport	small_airport	35.976330	139.524125	NaN	JP-11	Kawajima	POINT (1405226.731 1219424.722)
395	Mint Airstrip	small_airport	36.002160	139.498000	NaN	JP-11	Kawajima	POINT (1402372.569 1221933.565)
397	Yuhikoku Kawajima Airstrip	small_airport	36.011448	139.500289	NaN	JP-11	Kawajima	POINT (1402411.305 1223009.37)
394	Tokyo Flying Club Airstrip	small_airport	36.035460	139.500880	NaN	JP-11	Konosu	POINT (1402025.772 1225711.421)
401	Air Wing Motor Paraglider School Osato Landing...	small_airport	36.087990	139.431460	NaN	JP-11	Kumagaya	POINT (1394744.455 1230573.005)
361	Iwabuchi Watergate Auxiliary Airfield	small_airport	35.782031	139.738133	NaN	JP-13	Kita, Tokyo	POINT (1428344.169 1200829.351)

	0
18	POLYGON ((633554.485 497789.938, 633481.88 497...
23	POLYGON ((665455.602 616562.567, 666384.034 61...
25	POLYGON ((661946.206 613112.93, 661751.342 613...
26	POLYGON ((608598.171 611463.601, 608476.211 61...
27	POLYGON ((599133.659 618899.535, 599066.47 618...
...	...
4779	POLYGON ((1430008.535 2301665.045, 1430020.051...
4786	POLYGON ((1429256.295 2305122.691, 1429444.566...
4787	POLYGON ((1392111.274 2304170.183, 1391609.272...
4790	POLYGON ((1389739.838 2308307.381, 1389774.041...
4791	POLYGON ((1389427.709 2306943.308, 1389401.863...

	name	kind	latitude_deg	longitude_deg	elevation_ft	iso_region	municipality	geometry
1303	Taragi Auxiliary Airfield	small_airport	32.276669	130.915607	NaN	JP-43	Taragi	POINT (652126.25 716707.08)
467	Shirakawa Gliderport	small_airport	32.776029	130.630532	NaN	JP-43	Kumamoto	POINT (621720.608 770527.433)
3508	Ozuki Airfield	small_airport	34.045300	131.052002	13.0	JP-35	Shimonoseki	POINT (651703.34 914283.914)
469	Hofu Gliderport	small_airport	34.039370	131.540310	NaN	JP-35	Hofu	POINT (696972.88 916865.687)
855	Ryu's Skypark	small_airport	34.638053	136.545568	NaN	JP-24	Tsu	POINT (1153412.048 1029972.088)
...	...	...	...	...	...	...	...	...
1653	Yausubetsu Airfield	small_airport	43.287200	144.997800	108.0	JP-01	Betsukai	POINT (1705595.702 2136981.909)
466	Betsukai Flight Park	small_airport	43.474840	144.783090	407.0	JP-01	Betsukai	POINT (1682695.261 2153291.301)
460	Kitami Agricultural Airfield / Skyport Kitami	small_airport	43.779980	143.730640	609.0	JP-01	Kitami	POINT (1589025.95 2165571.973)
1660	Otaki Airfield	small_airport	42.665802	141.101933	1621.0	JP-01	Date	POINT (1403367.264 1992761.454)
463	Takikawa Sky Park	small_airport	43.549349	141.894103	75.0	JP-01	Takikawa	POINT (1446781.404 2105034.171)

	ADM0_EN	ADM0_JA	ADM0_PCODE	ADM1_EN	ADM1_JA	ADM1_PCODE	geometry
0	Japan	日本	JP	Aichi	愛知県	JP23	MULTIPOLYGON (((1195512.712 1039956.796, 11953...
1	Japan	日本	JP	Akita	秋田県	JP05	MULTIPOLYGON (((1376309.983 1576859.486, 13757...
2	Japan	日本	JP	Aomori	青森県	JP02	MULTIPOLYGON (((1488100.706 1763108.389, 14877...
3	Japan	日本	JP	Chiba	千葉県	JP12	MULTIPOLYGON (((1466610.222 1182881.245, 14660...
4	Japan	日本	JP	Ehime	愛媛県	JP38	MULTIPOLYGON (((801590.554 801225.044, 802058....

	name	kind	latitude_deg	longitude_deg	elevation_ft	iso_region	municipality	geometry
0	New Ishigaki Airport	medium_airport	24.396389	124.245000	102.0	JP-47	Ishigaki	POINT (24849.797 -177601.73)
1	JMSDF Chichijima Airfield	seaplane_base	27.090231	142.192032	NaN	JP-13	Ogasawara	POINT (1821915.408 255473.101)
2	Urayasu Heliport	heliport	35.623658	139.894506	16.0	JP-12	Urayasu	POINT (1445601.261 1185419.126)
3	Yagishiri Emergency Heliport	heliport	44.439286	141.400487	NaN	JP-01	Haboro	POINT (1385302.891 2195212.393)
4	Yokohama Heliport	heliport	35.342222	139.656389	4.0	JP-14	Yokohama	POINT (1428917.497 1150234.837)

	0
name	object
kind	object
latitude_deg	float64
longitude_deg	float64
elevation_ft	float64
iso_region	object
municipality	object
geometry	geometry